SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5101–5150 of 15113 papers

TitleStatusHype
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems—0
HoME: a Household Multimodal Environment—0
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat—0
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents—0
HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare—0
Horizon: Facebook's Open Source Applied Reinforcement Learning Platform—0
Horizon-Free Regret for Linear Markov Decision Processes—0
Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes—0
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies—0
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
How Can Creativity Occur in Multi-Agent Systems?—0
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study—0
How does AI play football? An analysis of RL and real-world football strategies—0
How does the structure embedded in learning policy affect learning quadruped locomotion?—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
How do Offline Measures for Exploration in Reinforcement Learning behave?—0
How hard is my MDP?" The distribution-norm to the rescue"—0
How many weights are enough : can tensor factorization learn efficient policies ?—0
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning—0
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?—0
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning—0
How to Combine Tree-Search Methods in Reinforcement Learning—0
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies—0
How to Enable Uncertainty Estimation in Proximal Policy Optimization—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
How to Leverage Unlabeled Data in Offline Reinforcement Learning—0
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology—0
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation—0
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks—0
How To Train Your HERON—0
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned—0
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory—0
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications—0
How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning—0
HRLAIF: Improvements in Helpfulness and Harmlessness in Open-domain Reinforcement Learning From AI Feedback—0
Human-Agent Cooperation in Bridge Bidding—0
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning—0
Human AI interaction loop training: New approach for interactive reinforcement learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning—0
Human-centered collaborative robots with deep reinforcement learning—0
Human-centered mechanism design with Democratic AI—0
Human-centric Dialog Training via Offline Reinforcement Learning—0
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment—0
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning—0
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified